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Deng S, Li C, Cao J, Cui Z, Du J, Fu Z, Yang H, Chen P. Organ-on-a-chip meets artificial intelligence in drug evaluation. Theranostics 2023; 13:4526-4558. [PMID: 37649608 PMCID: PMC10465229 DOI: 10.7150/thno.87266] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 08/02/2023] [Indexed: 09/01/2023] Open
Abstract
Drug evaluation has always been an important area of research in the pharmaceutical industry. However, animal welfare protection and other shortcomings of traditional drug development models pose obstacles and challenges to drug evaluation. Organ-on-a-chip (OoC) technology, which simulates human organs on a chip of the physiological environment and functionality, and with high fidelity reproduction organ-level of physiology or pathophysiology, exhibits great promise for innovating the drug development pipeline. Meanwhile, the advancement in artificial intelligence (AI) provides more improvements for the design and data processing of OoCs. Here, we review the current progress that has been made to generate OoC platforms, and how human single and multi-OoCs have been used in applications, including drug testing, disease modeling, and personalized medicine. Moreover, we discuss issues facing the field, such as large data processing and reproducibility, and point to the integration of OoCs and AI in data analysis and automation, which is of great benefit in future drug evaluation. Finally, we look forward to the opportunities and challenges faced by the coupling of OoCs and AI. In summary, advancements in OoCs development, and future combinations with AI, will eventually break the current state of drug evaluation.
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Affiliation(s)
- Shiwen Deng
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Caifeng Li
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Robot Intelligent Laboratory of Traditional Chinese Medicine, Experimental Research Center, China Academy of Chinese Medical Sciences & MEGAROBO, Beijing 100700, China
| | - Junxian Cao
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Zhao Cui
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Jiang Du
- Yunnan Biovalley Pharmaceutical Co., Ltd, Kunming 650503, China
| | - Zheng Fu
- Robot Intelligent Laboratory of Traditional Chinese Medicine, Experimental Research Center, China Academy of Chinese Medical Sciences & MEGAROBO, Beijing 100700, China
| | - Hongjun Yang
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Robot Intelligent Laboratory of Traditional Chinese Medicine, Experimental Research Center, China Academy of Chinese Medical Sciences & MEGAROBO, Beijing 100700, China
| | - Peng Chen
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Yunnan Biovalley Pharmaceutical Co., Ltd, Kunming 650503, China
- Robot Intelligent Laboratory of Traditional Chinese Medicine, Experimental Research Center, China Academy of Chinese Medical Sciences & MEGAROBO, Beijing 100700, China
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Comprehensive assessment of myocardial remodeling in ischemic heart disease by synchrotron propagation based X-ray phase contrast imaging. Sci Rep 2021; 11:14020. [PMID: 34234175 PMCID: PMC8263575 DOI: 10.1038/s41598-021-93054-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 06/16/2021] [Indexed: 02/06/2023] Open
Abstract
Cardiovascular research is in an ongoing quest for a superior imaging method to integrate gross-anatomical information with microanatomy, combined with quantifiable parameters of cardiac structure. In recent years, synchrotron radiation-based X-ray Phase Contrast Imaging (X-PCI) has been extensively used to characterize soft tissue in detail. The objective was to use X-PCI to comprehensively quantify ischemic remodeling of different myocardial structures, from cell to organ level, in a rat model of myocardial infarction. Myocardial infarction-induced remodeling was recreated in a well-established rodent model. Ex vivo rodent hearts were imaged by propagation based X-PCI using two configurations resulting in 5.8 µm and 0.65 µm effective pixel size images. The acquired datasets were used for a comprehensive assessment of macrostructural changes including the whole heart and vascular tree morphology, and quantification of left ventricular myocardial thickness, mass, volume, and organization. On the meso-scale, tissue characteristics were explored and compared with histopathological methods, while microstructural changes were quantified by segmentation of cardiomyocytes and calculation of cross-sectional areas. Propagation based X-PCI provides detailed visualization and quantification of morphological changes on whole organ, tissue, vascular as well as individual cellular level of the ex vivo heart, with a single, non-destructive 3D imaging modality.
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Huang J, Wang L, Chu C, Liu W, Zhu Y. Accelerating cardiac diffusion tensor imaging combining local low-rank and 3D TV constraint. MAGMA (NEW YORK, N.Y.) 2019; 32:407-422. [PMID: 30903326 DOI: 10.1007/s10334-019-00747-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Revised: 03/08/2019] [Accepted: 03/11/2019] [Indexed: 06/09/2023]
Abstract
OBJECTIVE Diffusion tensor magnetic resonance imaging (DT-MRI, or DTI) is a promising technique for invasively probing biological tissue structures. However, DTI is known to suffer from much longer acquisition time with respect to conventional MRI and the problem is worsened when dealing with in vivo acquisitions. Therefore, faster DTI for both ex vivo and in vivo scans is highly desired. MATERIALS AND METHODS This paper proposes a new compressed sensing (CS) reconstruction method that employs local low-rank (LLR) model and three-dimensional (3D) total variation (TV) constraint to reconstruct cardiac diffusion-weighted (DW) images from highly undersampled k-space data. The LLR model takes the set of DW images corresponding to different diffusion gradient directions as a 3D image volume and decomposes the latter into overlapping 3D blocks. Then, the 3D blocks are stacked as two-dimensional (2D) matrix. Finally, low-rank property is applied to each block matrix and the 3D TV constraint to the 3D image volume. The underlying constrained optimization problem is finally solved using the first-order fast method. The proposed method is evaluated on real ex vivo cardiac DTI data as a prerequisite to in vivo cardiac DTI applications. RESULTS The results on real human ex vivo cardiac DTI images demonstrate that the proposed method exhibits lower reconstruction errors for DTI indices, including fractional anisotropy (FA), mean diffusivities (MD), transverse angle (TA), and helix angle (HA), compared to existing CS-based DTI image reconstruction techniques. CONCLUSION The proposed method provides better reconstruction quality and more accurate DTI indices in comparison with the state-of-the-art CS-based DW image reconstruction methods.
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Affiliation(s)
- Jianping Huang
- College of Mechanical and Electrical Engineering, Northeast Forestry University, Heilongjiang, 150040, Harbin, China.
- Metislab, LIA CNRS, Harbin Institute of Technology, Heilongjiang, 150001, Harbin, China.
- CREATIS, CNRS UMR5220, Inserm U1206, INSA Lyon, University of Lyon, Lyon, France.
| | - Lihui Wang
- Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Chunyu Chu
- College of Engineering, Bohai University, Jinzhou, 121013, China
| | - Wanyu Liu
- Metislab, LIA CNRS, Harbin Institute of Technology, Heilongjiang, 150001, Harbin, China
| | - Yuemin Zhu
- Metislab, LIA CNRS, Harbin Institute of Technology, Heilongjiang, 150001, Harbin, China
- CREATIS, CNRS UMR5220, Inserm U1206, INSA Lyon, University of Lyon, Lyon, France
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Zhou FL, Li Z, Gough JE, Hubbard Cristinacce PL, Parker GJ. Axon mimicking hydrophilic hollow polycaprolactone microfibres for diffusion magnetic resonance imaging. MATERIALS & DESIGN 2018; 137:394-403. [PMID: 29307950 PMCID: PMC5727678 DOI: 10.1016/j.matdes.2017.10.047] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Revised: 10/12/2017] [Accepted: 10/14/2017] [Indexed: 06/07/2023]
Abstract
Highly hydrophilic hollow polycaprolactone (PCL) microfibres were developed as building elements to create tissue-mimicking test objects (phantoms) for validation of diffusion magnetic resonance imaging (MRI). These microfibres were fabricated by the co-electrospinning of PCL-polysiloxane-based surfactant (PSi) mixture as shell and polyethylene oxide as core. The addition of PSi had a significant effect on the size of resultant electrospun fibres and the formation of hollow microfibres. The presence of PSi in both co-electrospun PCL microfibre surface and cross-section, revealed by X-ray energy dispersive spectroscopy (EDX), enabled water to wet these fibres completely (i.e., zero contact angle) and remained active for up to 12 months after immersing in water. PCL and PCL-PSi fibres with uniaxial orientation were constructed into water-filled phantoms. MR measurement revealed that water molecules diffuse anisotropically in the PCL-PSi phantom. Co-electrospun hollow PCL-PSi microfibres have desirable hydrophilic properties for the construction of a new generation of tissue-mimicking dMRI phantoms.
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Affiliation(s)
- Feng-Lei Zhou
- Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester M13 9PT, United Kingdom
- CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, UK
- The School of Materials, The University of Manchester, Manchester M13 9PL, United Kingdom
| | - Zhanxiong Li
- College of Textile and Clothing Engineering, Soochow University, Suzhou 215000, PR China
| | - Julie E. Gough
- The School of Materials, The University of Manchester, Manchester M13 9PL, United Kingdom
| | | | - Geoff J.M. Parker
- Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester M13 9PT, United Kingdom
- CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, UK
- Bioxydyn Limited, Rutherford House, Manchester Science Park, Pencroft Way, Manchester M15 6SZ, United Kingdom
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Bakermans AJ, Abdurrachim D, Moonen RPM, Motaal AG, Prompers JJ, Strijkers GJ, Vandoorne K, Nicolay K. Small animal cardiovascular MR imaging and spectroscopy. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2015; 88-89:1-47. [PMID: 26282195 DOI: 10.1016/j.pnmrs.2015.03.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2014] [Revised: 03/09/2015] [Accepted: 03/09/2015] [Indexed: 06/04/2023]
Abstract
The use of MR imaging and spectroscopy for studying cardiovascular disease processes in small animals has increased tremendously over the past decade. This is the result of the remarkable advances in MR technologies and the increased availability of genetically modified mice. MR techniques provide a window on the entire timeline of cardiovascular disease development, ranging from subtle early changes in myocardial metabolism that often mark disease onset to severe myocardial dysfunction associated with end-stage heart failure. MR imaging and spectroscopy techniques play an important role in basic cardiovascular research and in cardiovascular disease diagnosis and therapy follow-up. This is due to the broad range of functional, structural and metabolic parameters that can be quantified by MR under in vivo conditions non-invasively. This review describes the spectrum of MR techniques that are employed in small animal cardiovascular disease research and how the technological challenges resulting from the small dimensions of heart and blood vessels as well as high heart and respiratory rates, particularly in mice, are tackled.
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Affiliation(s)
- Adrianus J Bakermans
- Biomedical NMR, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Desiree Abdurrachim
- Biomedical NMR, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Rik P M Moonen
- Biomedical NMR, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Abdallah G Motaal
- Biomedical NMR, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Jeanine J Prompers
- Biomedical NMR, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Gustav J Strijkers
- Biomedical NMR, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Katrien Vandoorne
- Biomedical NMR, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Klaas Nicolay
- Biomedical NMR, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
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